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024 7 _ |a 10.1109/AICCSA56895.2022.10017883
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024 7 _ |a 2128/33797
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024 7 _ |a WOS:000932894200052
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037 _ _ |a FZJ-2022-06229
041 _ _ |a English
100 1 _ |a Alia, Ahmed
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111 2 _ |a 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications
|g AICCSA
|c Zayed University, Abu Dhabi
|d 2022-12-05 - 2022-12-07
|w U Arab Emirates
245 _ _ |a A Fast Hybrid Deep Neural Network Model for pushing behavior detection in human crowds
260 _ _ |c 2023
|b IEEE
300 _ _ |a 1-2
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520 _ _ |a Deep learning technology is regarded as one of the latest advances in data science and analytics due to its learning abilities from the data. As a result, deep learning is widely applied in the human crowd analysis domain. Although it has achieved remarkable success in this area, a fast and robust model for pushing behavior detection in the human crowd is unavailable. This paper proposes a model that allows crowd-monitoring systems to detect pushing behavior early, helping organizers make timely decisions before dangerous situations appear. This particularly becomes more challenging when applied to real-time video streams of crowded events, which the proposed model accomplishes with reasonable time latency. To achieve this, the model employs a hybrid deep neural network.
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700 1 _ |a Maree, Mohammed
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700 1 _ |a Chraibi, Mohcine
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773 _ _ |a 10.1109/AICCSA56895.2022.10017883
856 4 _ |u https://ieeexplore.ieee.org/document/10017883
856 4 _ |u https://juser.fz-juelich.de/record/916435/files/Ahmed%20Alia%20Extended%20Abstract-AICCSA2022.pdf
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